Collaborative modeling environment

ABSTRACT

A system may receive a model, extract information from the model, form a group of tags using the extracted information, and associate the group of tags with the model. The system may further receive a search query including one or more sequences of characters and determine whether to provide the model in a list of models created for the search query, based on the one or more sequences of characters and the group of tags.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.11/687,510, filed Mar. 16, 2007, the disclosure of which is incorporatedby reference herein in its entirety.

BACKGROUND INFORMATION

Models may be designed for numerical simulations or for other purposes.In some instances, a model designer may need to design a portion of amodel (e.g., a pump of a waste water treatment plant) with which themodel designer does not have a high level of familiarity. The modeldesigner may make assumptions regarding the unfamiliar portion that aredifferent than the way the portion actually works. Thus, the model maynot be completely accurate.

SUMMARY OF THE INVENTION

In one embodiment, a computer-readable medium may store instructionsexecutable by at least one processor for searching for models. Thecomputer-readable medium may include instructions for receiving a searchquery from a model creation environment; instructions for performing asearch to identify a list of models from a model repository based on thesearch query; instructions for providing the identified list of models;instructions for receiving a request for one model in the identifiedlist of models; and instructions for providing the one model to themodel creation environment based on receiving the request.

In another embodiment, a computer-readable medium may store instructionsexecutable by at least one processor. The computer-readable medium mayinclude instructions for receiving a model from a user device;instructions for obtaining information for the received model byexecuting the received model; and instructions for creating first tagsusing the obtained information, the created tags being at least one or acombination of displayed to a user or used as part of a model search.

In another embodiment, a computer-readable medium may store instructionsexecutable by at least one processor. The computer-readable medium mayinclude instructions for receiving a model from a user device;instructions for obtaining information for the received model, theobtained information including at least one or a combination of: anumber of input ports for the model, one or more characteristics of theinput ports, a number of output ports for the model, one or morecharacteristics of the output ports, whether the model uses continuoustime integration or discrete time integration, whether the model isself-contained, information identifying a number of subsystems withinthe model, a number of charts in the model, a number of discrete statesin the model, whether the model adheres to one or more modelingstandards, annotations added to the model, or review information aboutan author of the model; and instructions for at least one or acombination of displaying the obtained information to a user or usingthe obtained information to determine whether the received model relatesto a search query.

In another embodiment, a computer-readable medium may store instructionsexecutable by at least one processor. The computer-readable medium mayinclude instructions for detecting a single selection of an element in amodel creation environment that includes a model and instructions fortransmitting a portion of the model to a modeling infrastructure inresponse to detecting the single selection. The transmitting may causethe portion of the model to be published by the modeling infrastructure.

In still another embodiment, a computer-readable medium may storeinstructions executable by at least one processor. The computer-readablemedium may include instructions for detecting selection of an element ina model creation environment; instructions for providing a search dialogbox in response to detecting the selection, where the search dialog boxallows for a model search to be performed; instructions for receiving asearch query; and instructions for transmitting information relating tothe search query to a remote location.

In still yet another embodiment, a computer-readable medium may storeinstructions executable by at least one processor. The computer-readablemedium may include instructions for receiving a search query;instructions for identifying a list of models in response to receivingthe search query, the identifying including relating the search query toinformation extracted from the identified models; and instructions forproviding the list of models to a user.

In another embodiment, a computer-readable medium may store instructionsexecutable by at least one processor. The computer-readable medium mayinclude instructions for receiving a model; instructions for extractinginformation from the model; instructions for forming a group of tagsusing the extracted information; instructions for associating the groupof tags with the model; instructions for receiving a search queryincluding one or more sequences of characters; and instructions fordetermining whether to provide the model in a list of models created forthe search query based on the one or more sequences of characters andthe group of tags.

In yet another embodiment, a computer-readable medium may store storesinstructions executable by at least one processor. The computer-readablemedium may include instructions for automatically searching for models;instructions for retrieving a model in response to the searching; andinstructions for executing the model using a program to determinewhether an issue exists with the model or with the program.

In still yet another embodiment, a computer-readable medium may storeinstructions executable by at least one processor. The computer-readablemedium may include instructions for providing access to a model to aremote model creation environment and instructions for transmitting arating for the model to the remote model creation environment fordisplay in the remote model creation environment.

In another embodiment, a computing device-implemented method may includereceiving a model at a modeling infrastructure; making the modelavailable for peer review; and publishing, via the modelinginfrastructure, based on the peer review.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate one or more embodiments and,together with the description, explain these embodiments. In thedrawings,

FIG. 1 is an exemplary diagram illustrating a concept consistent withexemplary embodiments;

FIG. 2 is an exemplary diagram of a network in which systems and methodsconsistent with exemplary embodiments may be implemented;

FIG. 3 is an exemplary diagram of the user device of FIG. 2 in anexemplary embodiment;

FIG. 4 is an exemplary diagram of the modeling infrastructure of FIG. 2in an exemplary embodiment;

FIG. 5 is a flowchart of an exemplary process for sharing a model in anexemplary embodiment;

FIGS. 6A-6C are exemplary documents that may be provided to allow a userto share a model;

FIG. 7 is an exemplary notification that may be provided to a user if auser's attempt to share a model is unsuccessful;

FIG. 8 is a flowchart of an exemplary process for associatinginformation with a model;

FIGS. 9A and 9B are exemplary documents that may be provided to allow auser to provide information for a model;

FIG. 10 is a flowchart of an exemplary process for associating a ratingwith a model;

FIGS. 11A and 11B are exemplary documents that may be provided to allowa user to provide rating information for a model;

FIG. 12 is a flowchart of an exemplary process for obtaining a model;

FIGS. 13A-13D are exemplary documents that may be provided to a user inrelation to obtaining a model;

FIG. 14 is an exemplary document that may be provided to a user inrelation to obtaining a model;

FIG. 15 is an exemplary document that may be provided to a user inrelation to obtaining a model; and

FIGS. 16A and 16B illustrate how models in a model repository may beaccessed from a model creation environment.

DETAILED DESCRIPTION

The following detailed description of exemplary embodiments refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements. Also, the followingdetailed description does not limit the invention.

Overview

Systems and methods described herein may provide a modelinginfrastructure that allows users to make models available to other usersand to obtain models of interest. FIG. 1 is an exemplary diagramillustrating a concept 100 consistent with exemplary embodiments. Asillustrated in FIG. 1, a modeling infrastructure may receive a model(such as a Simulink® modeling environment model) from a user and processthe model to allow the model to be shared with other users. In oneembodiment, the modeling infrastructure may allow a user to share amodel with other users (e.g., publish the model) via a “1-click” (orsimplified) process. In this embodiment, the modeling infrastructure mayextract information from the received model and associate the extractedinformation with the model (e.g., as tags). The modeling infrastructuremay also allow users to search and obtain models that have been shared(e.g., uploaded to the modeling infrastructure and/or indicated to themodeling infrastructure as available for sharing). In essence, themodeling infrastructure may provide a model sharing ecosystem whereusers can easily share models with other users and obtain models ofinterest. In some embodiments, users may share comments regarding modelsthat are available via the modeling infrastructure and may rate thesemodels. In this way, users can use other users' opinions to distinguishbetween two possible models of interest.

A “document,” as the term is used herein, is to be broadly interpretedto include any machine-readable and/or machine-storable work product. Adocument may include, for example, an e-mail, a file, a dialog, acombination of files, one or more files with embedded links to otherfiles, a graphical user interface, etc. In the context of the Internet,a common document is a web page. Documents often include textualinformation and may include embedded information (such as metainformation, images, hyperlinks, etc.) and/or embedded instructions(such as Javascript, etc.). A “tag” as used herein may broadly includeany type of data that may be used to relate information to an object. Anexample of a tag may include a meta tag or another type of tag. A“model” may be broadly defined as a representation of something (e.g.,an object, a system, etc.). A “computational model” may be broadlydefined as a model that contains an element that represents computation.A computational model may be associated with static and/or dynamicsemantics. The computational model may include entities. Relationsbetween the entities may be explicitly represented. In graphical models,these relations may be represented as lines. In textual models, theserelations may represented based on the order of syntactic entities in asentence. A computational model with dynamic semantics can beinterpreted by an execution engine to generate a behavior, where abehavior refers to a change of value of a variable. Dynamic models mayinclude one or more data structures to represent elements that captureaspects of the dynamics (e.g., the behavior). Examples of such aspectsmay include the identity of the values that represent how a new value ofa variable is computed (e.g., the value of a derivative of a variablewith respect to time or a time delayed value of a variable), how thesequence of values is constructed, whether it is as a relation with anindependent variable (e.g., time), whether the values should be computedwith a given period, etc. As such in a dynamic model, a variable canassume multiple values when evaluated. These aspects for different modelelements are often related, for example, to ensure integrity andconsistency, and can be automatically derived and reconciled by modelprocessing.

Exemplary Network Configuration

FIG. 2 is an exemplary diagram of a network 200 in which systems andmethods described herein may be implemented. Network 200 may includemultiple user devices 210 connected to a modeling infrastructure 220 viaa network 230. Two user devices 210 and one modeling infrastructure 220have been illustrated as connected to network 230 for simplicity. Inpractice, there may be more or fewer user devices and more modelinginfrastructures. Also, in some instances, a user device may perform afunction that is described as being performed by the modelinginfrastructure and the modeling infrastructure may perform a functionthat is described as being performed by a user device.

User devices 210 may include user entities. An entity may be defined asa device, such as a personal computer, a laptop, a personal digitalassistant (PDA), a smartphone, or another type of computation orcommunication device, a thread or process running on one of thesedevices, and/or an object executable by one of these devices. Modelinginfrastructure 220 may include one or more hardware and/or softwarecomponents that receive models and information relating to models fromuser devices 210 and provide user devices 210 with the ability to accessthe models.

Network 230 may include a local area network (LAN), a wide area network(WAN), a metropolitan area network (MAN), a telephone network, such asthe Public Switched Telephone Network (PSTN) or a cellular network, anintranet, the Internet, or a combination of networks. User devices 210and modeling infrastructure 220 may connect to network 230 via wiredand/or wireless connections.

It will be appreciated that a user device 210 may perform one or moreacts described below as being performed by modeling infrastructure 220and that modeling infrastructure 220 may perform one or more actsdescribed below as being performed by a user device 210.

Exemplary User Device Architecture

FIG. 3 is an exemplary diagram of a user device 210. As illustrated,user device 210 may include a bus 310, a processor 320, a main memory330, a read only memory (ROM) 340, a storage device 350, an input device360, an output device 370, and a communication interface 380. Bus 310may include a path that permits communication among the elements of userdevice 210.

Processor 320 may include a processor, microprocessor, or processinglogic that may interpret and execute instructions. Main memory 330 mayinclude a random access memory (RAM) or another type of dynamic storagedevice that may store information and instructions for execution byprocessor 320. ROM 340 may include a ROM device or another type ofstatic storage device that may store static information and instructionsfor use by processor 320. Storage device 350 may include a magneticand/or optical recording medium and its corresponding drive.

Input device 360 may include a mechanism that permits an operator toinput information to user device 210, such as a keyboard, a mouse, apen, voice recognition and/or biometric mechanisms, an accelerometer orgyroscope-based motion input device, a camera, etc. Output device 370may include a mechanism that outputs information to the operator,including a display, a printer, a speaker, etc. Communication interface380 may include any transceiver-like mechanism that enables user device210 to communicate with other devices and/or systems. For example,communication interface 380 may include mechanisms for communicatingwith modeling infrastructure 220 via a network, such as network 230.

As will be described in detail below, user device 210, consistent withexemplary embodiments, may perform certain processing-relatedoperations. User device 210 may perform these operations in response toprocessor 320 executing software instructions contained in acomputer-readable medium, such as memory 330. A computer-readable mediummay be defined as a physical or logical memory device and/or carrierwave.

The software instructions may be read into memory 330 from anothercomputer-readable medium, such as data storage device 350, or fromanother device via communication interface 380. The softwareinstructions contained in memory 330 may cause processor 320 to performprocesses that will be described later. Alternatively, hardwiredcircuitry may be used in place of or in combination with softwareinstructions to implement processes described herein. Thus, embodimentsdescribed herein are not limited to any specific combination of hardwarecircuitry and software.

Exemplary Modeling Infrastructure Architecture

FIG. 4 is an exemplary diagram of modeling infrastructure 220. As shownin FIG. 4, modeling infrastructure 220 may include a user interfacemodule 410, a model ratings module 420, a model execution module 430, atag extraction module 440, a search module 450, and a model database460. User interface module 410, model ratings module 420, modelexecution module 430, tag extraction module 440, search module 450, andmodel database 460 may be implemented as software and/or hardwarecomponents within a single server entity, or as software and/or hardwarecomponents distributed across multiple server entities. In oneembodiment, one or more of these modules may be implemented partly orwholly by an individual.

User interface module 410 may include one or more software and/orhardware components that provide documents to user devices. For example,in one embodiment, user interface module 410 may provide documents thatallow users to search for and obtain models of interest.

Model ratings module 420 may include one or more software and/orhardware components that allow users to rate models that are associatedwith model infrastructure 220. In one embodiment, model ratings module420 may receive rating information from a user and may determine arating for a model based on the received rating information. In someembodiments, model ratings module 420 may determine more than one ratingfor a model. Each rating may be associated with a different ratingcharacteristic, such as operating speed, memory usage, sensitivity todisturbances, number of zero crossings, performance on Japaneseplatform, etc. Thus, as an example, the ratings associated with a modelmay indicate that the model “runs slowly,” “takes a lot of memory,” “isvery sensitive to disturbances,” “includes a lot of zero crossings,”“cannot run well on Japanese platform,” etc.

Model execution module 430 may include one or more software and/orhardware components that determine whether models function properly. Inone embodiment, model execution module 430 may receive or access a modeland may execute the model to verify the model's functional status. Ifthe model does not function properly (e.g., an error is produced whenthe model is executed), model execution module 430 may, for example,notify the user that the model cannot be shared.

Tag extraction module 440 may include one or more software and/orhardware components that create tags (or other forms of data that allowfor searching of models) for a given model. In one embodiment, tagexecution module 440 may receive a model, analyze the model to identifycharacteristics of the model, and associate the extracted information astags with the model. The information may include any information thatwould be of interest to a user searching for a model. For example, theinformation may include information indicating the complexity (e.g.,cyclomatic complexity) of the model, the number of warnings that anediting style is violated, the number of input ports for the model andcharacteristics of the input ports, the number of output ports for themodel and characteristics of the output ports, whether the model usescontinuous time integration or discrete time integration, whether themodel is self-contained, information identifying the number ofsubsystems within the model, information identifying a product orproducts on which the model relies (e.g., a user will need to have orobtain a license for a particular product), annotations added to themodel, the software that was used to create the model, the version ofthe software that was used to create the model, whether the modeladheres to certain modeling standards (e.g., whether the model resultsin Motor Industry Software Reliability Association (MISRA) compliantcode, whether the model can be used in DO-178B certification, etc.), thecoverage of model functionality when executed (e.g., the percentdecision coverage, condition coverage, condition/decision coverage,modified condition/decision coverage, etc.), etc.

Search module 450 may include one or more software and/or hardwarecomponents that allow users to search for models. For example, searchmodule 450 may include a model search engine in which a user may entersearch terms and/or other search criteria. Search module 450 may use thesearch terms and/or search criteria to identify a list of candidatemodels.

Model database 460 may include one or more databases that may storemodels (which includes storing actual models, a portion of the models,links to models (e.g., in the form of uniform resource locators (URLs)),etc.) and information relating to models, such as tags, user reviews (orcomments), ratings, cost, etc. In some instances, model database 460 maybe considered a repository of models and information relating to models.In one embodiment, model database 460 may store a model, including allof the model's dependent files (e.g., parameter files), in a single fileor virtual group. The single file may include, for example, a singleproject, a single zipped file, a single archive, etc. In this way, auser can download a single file that includes all of the information forexecuting a model. Database 460 may include one or more physicaldatabases, which may be located at modeling infrastructure 220 or one ormore remote locations in network 200, or may represent a conceptualdatabase/repository of models.

Although FIG. 4 shows exemplary components of modeling infrastructure220, in other embodiments, modeling infrastructure 220 may includefewer, different, or additional components than depicted in FIG. 4.Moreover, one or more components of modeling infrastructure 220 mayperform one or more functions of one or more other components ofmodeling infrastructure 220.

Exemplary Processes

FIG. 5 is a flowchart of an exemplary process for sharing a model in anexemplary embodiment. The processing of FIG. 5 may be performed by oneor more software and/or hardware components within modelinginfrastructure 220. Alternatively, the processing may be performed byone or more software and/or hardware components within another device ora group of devices separate from or including modeling infrastructure220. It will be appreciated that the model sharing described herein mayactually correspond to sharing a portion of a model. The portion may bea stand-alone model that may be referenced in turn.

Processing may begin with modeling infrastructure 220 detecting anindication to share a model (block 510). Modeling infrastructure 220 mayreceive a request to share a model from user device 210 and, inresponse, may establish a connection with user device 210. In otherembodiments, a connection between modeling infrastructure 220 and userdevice 210 may be established before receiving the request. As part ofestablishing the connection with user device 210, modelinginfrastructure 220 may authenticate the user of user device 210. Forexample, modeling infrastructure 220 may request that the user of userdevice 210 provide a user identifier and possibly a password.

FIGS. 6A-6C are exemplary graphical user interfaces that may be providedto a user of user device 210. In FIG. 6A, assume a user has created amodel in a model creation environment, such as the Simulink® modelingenvironment. A document 600, associated with the model creationenvironment, may include a menu. The menu may include a menu element 610(called “SHARE”) that allows the user to share the model created by themodel creation environment. In one embodiment, menu element 610 may beassociated with menu items SHARE REMOTELY 620 and SHARE LOCALLY 630. Ifselected by the user, menu item SHARE REMOTELY 620 may allow the user tostore the created model at modeling infrastructure 220 (or provide alink to a remote location where the model is available) to make themodel available from modeling infrastructure 220. Menu item SHARELOCALLY 630, on the other hand, may allow the user to tag the createdmodel as shared to make the model available locally from user device210. By sharing the model locally, the model may be made visible toother user devices and the other user devices may obtain the model fromuser device 210.

As an alternative to (or in addition to) providing the ability to sharea model via a menu element, a user may share a model in other ways. Forexample, as illustrated in FIG. 6B, a user who has created a model mayright-click on the model to cause a pop-up menu 650 to appear in adocument 640. Similar to SHARE menu element 610, pop-up menu 650 may beassociated with a SHARE REMOTELY menu item and a SHARE LOCALLY menu itemthat may allow the user to perform the operations described above. Asyet another alternative and as illustrated in FIG. 6C, dedicated buttons670 and 680 may be provided in a document 660 to allow the user to sharethe model remotely and locally, respectively. In one embodiment, themodel that is to be shared may be represented by an element in anothermodel. For example, right-clicking on that element may cause a pop-upmenu to be displayed that allows only that element to be shared. If theelement is a referenced model, the interface information may beavailable. If the element is a subsystem, some model processing mayoccur to infer the interface information.

Returning to the processing of FIG. 5, modeling infrastructure 220 mayreceive the created model from user device 210 (block 520). In oneembodiment, user device 210 may transfer the created model to modelinginfrastructure 220 regardless of whether the user indicates that themodel is to be shared remotely or locally.

Modeling infrastructure 220 may execute the received model (block 530).Modeling infrastructure 220 may execute the model to determine whetherthe model is actually functional. If the model executes without an erroroccurring, modeling infrastructure 220 may determine that the model isfunctional. If, on the other hand, execution of the model produces anerror, modeling infrastructure 220 may determine that the model is notfunctional. In other embodiments, additional criteria or other criteriamay be used to determine if a received model is functional. Execution ofthe model may alternatively or additionally include processing thereceived model in other ways. For example, modeling infrastructure 220may perform a consistency check on the connections included in the modelto determine, for example, whether data types match, whether interfacetiming information matches, etc. In some embodiments, some informationmay be obtained by “compiling” the model, without executing the model.Compiling a model performs model processing to obtain all modelattributes to be used for execution (e.g., the sample rate of all blocksin a block diagram and the data types of all model variables). Compilinga model may also perform optimizations by removing superfluous modelelements, by combining computations such as replacing two consecutivemultiplications with constant factors by one multiplication with theaggregate factor, by reducing computations that provide the same resultfor each evaluation during a behavior to a constant value, etc.

If modeling infrastructure 220 determines the model is not functional(block 540—NO), modeling infrastructure 220 may notify the user that theattempt to share the model failed (block 550). FIG. 7 illustrates anexemplary notification message 710 that may be provided to the user ifan attempt to share the model failed. In some embodiments, notificationmessage 710 may include buttons, such as buttons 720 and 730, that allowthe user to continue to share the non-operational model in the mannerdescribed below or not share the non-operational model, respectively.Other ways of notifying the user are also possible. For example,modeling infrastructure 220 may transmit a notification via e-mail,instant message, or in another manner.

If modeling infrastructure 220 determines the model is functional (block540—YES), modeling infrastructure 220 may analyze the model to extractinformation regarding the model (block 560). This analysis may includeinterpreting the model, compiling the model, and/or executing the model.As indicated above, the extracted information may include anyinformation that a user may find important when searching for a model.For example, the extracted information may include informationindicating the complexity (e.g., cyclomatic complexity) of the model,the number of input ports for the model and characteristics of the inputports (e.g., data type, sample rate, etc.), the number of output portsfor the model and characteristics of the output ports, whether the modeluses continuous time integration or discrete time integration, whetherthe model is self-contained, information identifying the number ofsubsystems within the model, whether the model adheres to certainmodeling standards (e.g., whether the model results in MISRA compliantcode, whether the model can be used in DO-178B certification, etc.),information identifying a product or products on which the model relies(e.g., a user will need to have or obtain a license for a particularproduct), annotations added to the model, the software used to createthe model, the version of the software that was used to create themodel, information about an author of the model, and/or otherinformation. For example, other information that may be extracted mayinclude information identifying the number of state transition diagrams(charts) that are present in a model, information relating to statemachine elements (e.g., how many AND states are in the model, how manyOR states are in the model, how may substates are in the model, how manygraphical functions are in the model, how many history junctions are inthe model, how many junctions are in the model, how many events areregistered in the model, how many input events are in the model, howmany output events are in the model, characteristics of a state machineembedded in a block diagram, such as a sample rate, etc.), whether thestate transition diagram follows a style (such as Moore or Mealy),information relating to class diagrams that may be in the model (e.g.,how many classes, how deep the hierarchy is, whether there is multipleinheritance, whether interface classes are used, how many private,protected, and public elements are present, etc.), etc. For scenariodiagrams in the model, modeling infrastructure 220 may extractinformation relating to how many messages are passed between timelines,how many timelines there are, whether preemption is present, etc.Modeling infrastructure 220 may create tags using the extractedinformation (block 560). These tags may be considered as“machine-provided” tags.

Modeling infrastructure 220 may receive information regarding the modelfrom the user (block 570). The information may include, for example, theuser's description of the model (e.g., the operating range in which themodel should be executed), information about the user (e.g., contactinformation for the user (such as an e-mail address, an instantmessenger identifier, a telephone number, Internet Protocol (IP)address, voice over internet protocol (VoIP) address, etc.), the user'seducation, the number of models that the user has shared, etc.), and/orother information. In addition, the information may include any or allof the information that may be extracted from the model. Some or all ofthis user-provided information may be used to create tags (block 570).Moreover, the user may provide tags to be associated with the model. Theforegoing tags may be considered as “user-provided” tags.

Modeling infrastructure 220 may also receive information about the userfrom other users. For example, other users may provide informationindicating how prompt the user was in providing help regarding his/hermodel, whether the user produced good models in the past, whether theuser explains his/her models well, etc. Some or all of this informationmay also be used to create tags. These tags may also be considered asuser-provided tags.

Modeling infrastructure 220 may associate the machine-provided tags andthe user-provided tags with the model (block 580). Modelinginfrastructure 220 may further store the model. For example, in thosesituations where the user had indicated that the model is to be sharedremotely, modeling infrastructure 220 may store the model (or a link tothe model on a remote device) and the tags in database 460. In thosesituations where the user had indicated that the model is to be sharedlocally, modeling infrastructure 220 may discard the model and store alink to the model (e.g., on user device 210) and the tags in database460. Once the model has been processed by modeling infrastructure 220,the model can be considered as published (e.g., available for sharing).

In one embodiment, a model may undergo a review process, such as bypeers, prior to becoming published. For example, after some or all ofthe above processing, a received model may be submitted to one or moremodel evaluators to determine whether the model is acceptable forpublishing. In some situations, a model evaluator(s) may send a receivedmodel back to the author for revisions. The model may be revised untilthe model is accepted by the model evaluator(s). Once accepted by themodel evaluator(s), the model may be published. Moreover, additionaldocuments can be included with the model submission, such asdescriptions of how model parameters were derived, what laws of physicswere employed to design the model, explanations on potential and actualmodel use, what assumptions were made about the model, etc.

Once the model has been made available for sharing, modelinginfrastructure 220 may, in one embodiment, use a version control systemto manage revisions of the model. For example, changes to the model maybe identified by incrementing an associated revision number or lettercode.

In one embodiment, modeling infrastructure 220 may re-execute the modelwhen the model creation environment has been updated. Modelinginfrastructure 220 may send notifications based on a result of there-execution.

FIG. 8 is a flowchart of an exemplary process for associating a reviewwith a model in an exemplary embodiment. The processing of FIG. 8 may beperformed by one or more software and/or hardware components withinmodeling infrastructure 220. Alternatively, the processing may beperformed by one or more software and/or hardware components withinanother device or a group of devices separate from or including modelinginfrastructure 220.

Processing may begin with modeling infrastructure 220 receiving a reviewfor a model (block 810). Modeling infrastructure 220 may, for example,provide a document, such as document 900 (FIG. 9A), to a user of a userdevice 210. Document 900 may include an area 910 that allows the user tocomment on (or review) the model being displayed in document 900. Oncethe user has entered a comment (or review) in area 910, the user mayelect to post the comment by selecting button 920 or discard the commentby selecting button 930. In some embodiments, modeling infrastructure220 may request that the user log in to modeling infrastructure 220 by,for example, providing a user identifier and possibly a password, beforethe user is allowed to post a comment.

Modeling infrastructure 220 may associate the review with the model(block 820). For example, modeling infrastructure 220 may store thereview in database 460. Modeling infrastructure 220 may also associatethe review with information identifying the user who provided thereview. Thereafter, if the model is later viewed, user's review 940 maybe displayed, as illustrated in FIG. 9B.

FIG. 10 is a flowchart of an exemplary process for associating a ratingwith a model in an exemplary embodiment. The processing of FIG. 10 maybe performed by one or more software and/or hardware components withinmodeling infrastructure 220. Alternatively, the processing may beperformed by one or more software and/or hardware components withinanother device or a group of devices separate from or including modelinginfrastructure 220.

Processing may begin with modeling infrastructure 220 receiving ratinginformation for a model (block 1010). Modeling infrastructure 220 may,for example, provide a document, such as document 1100 (FIG. 11A), to auser of a user device 210. Document 1100 may include an area 1110 thatallows the user to rate the model being displayed in document 1100. Theuser may, for example, click on one of the stars to rate the model.Other techniques for rating a model may alternatively be used. Assume,as illustrated in FIG. 11A, that the user clicks on the right-most star(giving the model a rating of five stars). In some embodiments, modelinginfrastructure 220 may request that the user log in to modelinginfrastructure 220 by, for example, providing a user identifier andpossibly a password, before the user is allowed to rate a model.

Returning to the process of FIG. 10, modeling infrastructure 220 maydetermine a rating for the model using the received rating information(block 1020). For example, modeling infrastructure 220 may take theaverage of all of the ratings received for the model. Other techniquesfor determining a rating may alternatively be used. For example,modeling infrastructure 220 may take the mean and its variance of all ofthe ratings received for the model, the maximum and minimum of all ofthe ratings received for the model, etc.

Modeling infrastructure 220 may associate the determined rating with themodel (block 1030). For example, modeling infrastructure 220 may storethe determined rating in database 460. Thereafter, if a document isprovided that includes the model, a new rating 1120 may be displayed, asillustrated in FIG. 11B.

FIG. 12 is a flowchart of an exemplary process for obtaining a model.The processing of FIG. 12 may be performed by one or more softwareand/or hardware components within modeling infrastructure 220.Alternatively, the processing may be performed by one or more softwareand/or hardware components within another device or a group of devicesseparate from or including modeling infrastructure 220.

Processing may begin with modeling infrastructure 220 receiving a searchquery from a user of a user device 210 (block 1210). Assume, asillustrated in FIG. 13A, that a user is in the process of creating amodel using a model creation environment document 1300. The user mayinsert an element 1310 into the model where the user wants to insertanother model into the model being created. By selecting element 1310, asearch dialog box 1320 may be presented to the user. The user may entera search query into search dialog box 1320 to cause user device 210 totransmit the search query to modeling infrastructure 220. Search dialogbox 1320 may be associated with an advanced search feature and apreferences feature, as will be described in further detail below. Theuser may cause search dialog box 1320 to appear in other ways. Forexample, model creation environment 1300 may provide a menu item that,if selected, causes search dialog box 1320 to appear.

As an alternative to accessing a search dialog box via a model creationenvironment, the user may connect to modeling infrastructure 220 (oranother device) to access a search dialog box. For example, assume, asillustrated in FIG. 13B, a user connects to modeling infrastructure 220and requests a document 1330 that allows the user to search for a modelof interest. As illustrated in FIG. 13B, document 1330 may include asearch dialog box 1340, an advanced search feature 1350, and apreferences feature 1360. Search dialog box 1340 may allow the user toenter a search query for a model and transmit the search query tomodeling infrastructure 220 for processing. If advanced search feature1350 is selected (e.g., by clicking on it), modeling infrastructure 220may provide a document 1370, as illustrated in FIG. 13C. Document 1370may allow the user to specify criteria for performing a current searchfor a model. For example, document 1370 may allow the user to specifyone or more sequences of characters that may appear in a titleassociated with a model, one or more sequences of characters that mayappear in a user description of a model, a level of complexity for amodel, the number of input ports contained in the model and/or a rangefor the number of input ports in the model (e.g., less than 10, between5 and 10, more than 5, etc.), the number of output ports contained inthe model and/or a range for the number of output ports in the model(e.g., less than 10, between 5 and 10, more than 5, etc.),characteristics of the input ports and/or output ports (such as datatype, sample rate, etc.), a date or date range when the model was addedto modeling infrastructure 220, an author of a model associated withmodeling infrastructure 220, a location or group of locations (e.g., oneor more web sites) where the model search is to be performed, ratinginformation (such as “only provide models that have been rated as fourstars or above”), whether the model uses continuous time integration ordiscrete time integration, whether the model is self-contained, thenumber of subsystems within the model, the identify of the softwareand/or the version of the software that was used to create the model,and/or other information.

If preferences feature 1360 is selected (e.g., by clicking on it),modeling infrastructure 220 may provide a document 1380, as illustratedin FIG. 13D. Document 1380 may allow the user to specify criteria forperforming searches for a model. For example, document 1380 may allowthe user to specify a language in which document 1330 and otherdocuments are to be presented to the user, the language in which theuser is going to provide the search query, the number of results todisplay on a single page, and/or other information.

Returning to the process of FIG. 12, modeling infrastructure 220 mayidentify a list of models based on the received search query (block1220). Modeling infrastructure 220 may use any well known searchingtechnique to identify the list of models. The searching technique mayinvolve acts that are different than merely filtering a list of modelsbased, for example, on a name. For example, modeling infrastructure maymatch the one or more sequences of characters in the search query to thedata associated with the models (e.g., tags or other information).Modeling infrastructure 220 may also rank the models in the list based,for example, on how closely the models match the one or more sequencesof characters in the search query (or on other criteria).

Modeling infrastructure 220 may provide the list of models to the user(block 1230). For example, modeling infrastructure 220 may cause adocument, such as document 1400 illustrated in FIG. 14, which containsthe list of models, to be provided to the user. As illustrated in FIG.14, document 1400 may include an image section 1410, a title section1420, a description section 1430, a tags section 1440, an author section1450, a downloads section 1460, a cost section 1470, a rating section1480, and a reviews section 1490.

Image section 1410 may include a snapshot of the model (e.g., a highlevel block diagram or other image of the model). Alternatively, imagesection 1410 may include another image related or unrelated to the model(e.g., an image of the author of the model, an image selected by theauthor or another source, etc.). In one embodiment, document 1400 mayallow the user to execute a portion of the model by, for example,clicking on the snapshot in image section 1410. Title section 1420 mayinclude a title associated with the model. Description section 1430 mayinclude a description (or a portion of the description) that wasprovided by the author of the model. In other instances, descriptionsection 1430 may include a description (or a portion of the description)provided by another source (e.g., an administrator associated withmodeling infrastructure 220). Tags section 1440 may include a list ofone or more of the tags associated with the model. Author section 1450may include a name (or identifier) of the author of the model. In oneembodiment, selection of the author's name may cause further informationabout the user to be provided, such as background information and/orcontact information for the author. In another embodiment, selection ofthe author's name may cause an instant messenger or e-mail program to belaunched to allow the user to transmit a question or comment to theauthor. In yet another embodiment, the authors may be notified bymodeling infrastructure 220 upon occurrence of some events, such as, forexample, that one of the models of their creation has been downloaded.Downloads section 1460 may provide information regarding the number oftimes that users have downloaded (or obtained) the model. Cost section1470 may including information indicating how much it costs to download(or obtain) the model. The models may cost a certain amount of money ormay be free. In some embodiments, the models may be auctioned off to thehighest bidder or immediately sold to a bidder who offers a minimumamount. Rating section 1480 may indicate a rating associated with themodel. Reviews section 1490 may provide an indication of how many usershave reviewed the model. In one embodiment, selection of the number ofreviews may cause a document to be provided to the user that includesall or a portion of the reviews for the model. In one embodiment, someor all of the reviews may be selectively accessible to the user (e.g.,based on user privileges, a date stamp, etc.).

It will be appreciated that document 1400 may include additionalinformation or other information than illustrated in FIG. 14. Forexample, document 1400 may provide an indication of quality for a modelin document 1400 in a number of categories. The categories may includecyclomatic complexity of the model, the number of semantic and/or syntaxerrors in the model, the number of potential problems with the model,the amount of help files associated with the model, the number of issuesthat exist with respect to file naming, the amount of duplicatefunctionality or code included in the model, and/or other categories.Moreover, document 1400 may also provide an indication as to how themodel compares to other models in the above categories and/or othercategories.

Returning to the process of FIG. 12, modeling infrastructure 220 maydetect selection of one of the models provided in the list of models(block 1240). For example, the user may select a model by, for example,clicking on the image in image section 1410, the title in title section1420, or another portion of document 1400. In response to detectingselection of a model, modeling infrastructure 220 may provide additionaldetails regarding the model (block 1250). For example, modelinginfrastructure 220 may provide a document, such as document 1500illustrated in FIG. 15. In addition to the model information provided indocument 1400, document 1500 may provide a full description of the modelprovided by the user (or another user), reviews of the model, a largernumber of tags associated with the model, etc. Document 1500 may alsoinclude a button 1510 (or other element) that allows the user to obtainthe model.

Returning to the process of FIG. 12, modeling infrastructure 220 mayreceive a request for the model (block 1260). For example, assume thatthe user selects GET IT button 1510 in document 1500. In response, theuser's user device 210 may transmit a request for the model to modelinginfrastructure 220.

In those situations where the requested model is associated with a fee,the user may be requested to pay the fee prior to obtaining the model.The payment of the fee could be handled between the two parties directly(i.e., between the author of the model and the user purchasing themodel) or may involve a third-party mediator.

Modeling infrastructure 220 may provide the model to the user inresponse to the request (block 1270). In some embodiments, modelinginfrastructure 220 may request that the user log in to modelinginfrastructure 220 (e.g., by providing a user identifier and possibly apassword to modeling infrastructure 220) prior to providing a model. Inthose situations where the model is shared remotely (e.g., stored inmodeling infrastructure 220), modeling infrastructure 220 may allow theuser to download the model or connect to the model via, for example, aTransmission Control Protocol (TCP)/Internet Protocol (IP) connection orother type of connection. In those situations where the model is sharedlocally by the author, modeling infrastructure 220 may provideinstructions (or a link) for downloading the model from the author'suser device 210 or connecting to the model via, for example, a TCP/IPconnection or other type of connection. In either situation, theobtained model may be manually or automatically inserted into theelement (e.g., element 1310 in FIG. 13A) in the model that the user iscreating. In this way, a user may easily search for and obtain models ofinterest.

Transferring the requested model to the user (e.g., for inclusion inanother model being created by the user) may allow the model or part ofthe model to undergo some initial processing. For example, if the actualcontent of the model is available (rather than only an interface thatallows communication of numerical data, such as in co-simulation),inherited characteristics of the model can be supported. For example,the model (or part of the model) can inherit the sample rate from wherethe model is used. Since access to the model's internals is available,the sample rate of the context where the model is used can be used toestablish the sample rates of elements in the received model. This maybe accomplished by propagating the sample rate into the model (or partof the model). Also, optimizations may be applied to the model or modelpart. Moreover, since access to the model's internals is available, theinternals can be, for example, reduced in complexity while maintainingfunctionality. This reduction in complexity can be performed locally(i.e., within the model (or model part)), but also based on the contextin which the model (or model part) is used. For example, if the contextin which the model (or model part) is used is known, some of thefunctionality of the model (or model part) may be removed or the model(or model part) may be executed differently to obtain an overall moreefficient execution. In some embodiments, the model (or model part) maybe processed to obtain a more complex or simplified version. Forexample, nonlinear blocks within the model (or model part) may bereplaced by their linear approximation within a given tolerance that maybe user-provided.

In some embodiments, once the obtained model is inserted into theelement (e.g., element 1310 in FIG. 13A) or the reference to the modelis obtained, information, available through modeling infrastructure 220,regarding the obtained model may be displayed in the model creationenvironment. For example, in one embodiment, a rating of the model (asdetermined by modeling infrastructure 220) may be displayed on element1310. The rating may be represented by stars, numbers, or in anothermanner. The displayed rating may be dynamically updated as modelinginfrastructure 220 changes the rating for the obtained model. As anexample, a user may obtain a model that, at the time of obtaining themodel, is rated as 4 stars. Assume that over time, the rating of themodel decreases to 1 star. This decreased rating may be reflected in themodeling creation environment to the user who obtained the model. Thus,the user may decide, in this situation, to obtain another model due tothe low rating that the model has achieved. In an alternativeembodiment, the rating will continue to reflect the rating at the timewhen the user has initially obtained the model. In yet anotherembodiment, the rating may reflect both the initial rating and/or theupdated rating and the user's own rating. The proportional significanceof each of those parameters may be different in different embodiments.

The following example 1600 set forth in FIGS. 16A and 16B illustrateshow models in a model repository (e.g., models associated with modelinginfrastructure 220) (or models stored on a network, such as theInternet) may be accessed from a model creation environment. In example1600, the model creation environment may be used to design textualmodels. As illustrated in FIG. 16A, user interface 1610 may include twomain interface windows: a main window 1620 and a command window 1630.

Main window 1620 provides a user with the ability to access modelsassociated with modeling infrastructure 220. In example 1600, the modelrepository is marked “Home” and alternate repositories may beaccessible. Each repository may be associated, for example, with aseparate tab in main window 1620. Models may be grouped into categoriesin each repository. The models illustrated in main window 1620 are in a“Mathematic” category and may include, for example, minimum radius andminimum area based computations, Latex figure output, recursive spherecomputations, and integration methods. Additional models may bedisplayed by selecting arrow 1625 on the right-hand side of main window1620. Other categories that may be selected may include, for example, agraphics category, a visualization category, a programming category, andan “other categories” category. The models can be presented in mainwindow 1620 based on their time of creation, when the models were madeavailable (e.g., most recent first), or their ratings (e.g., based onhow high the ratings are, how often they have been rated, etc.), and/oron other information.

Command window 1630 may allow users to enter commands (e.g., at the “>>”prompt). The history of commands can be accessed by activating a CommandHistory tab 1635.

An additional details window 1640 that provides information regarding amodel in command window 1620 may be provided to the user, as illustratedin FIG. 16B, in response to a user selecting the model (e.g., inresponse to a mouse over, mouse click, tab event, or shortcut keyboardevent). As illustrated, additional details window 1640 may include thetitle of the model, an abstract to summarize an intent or content of themodel, one or more graphics (e.g., to display characteristic output ofthe model), the type of the model, the category to which the modelbelongs, when the model was made available (or published), informationidentifying an author or authors of the model, and a rating for themodel (e.g., based on a history of ratings). Furthermore, additionaldetails window 1640 may also include reviews in their complete orpartial form and/or a link to more reviews or more details of reviews.In example 1600, additional details window 1640 includes a link to opena document with the 5 reviews that the model has received. Additionaldetails window 1640 may include additional information, such as thenumber of inputs associated with the model, the number of outputsassociated with the model, the data types of inputs and outputs, thecomplexity, sample rates, dimensions, cyclomatic complexity, the numberof model elements of a certain type, such as, e.g., lines of code, etc.

Conclusion

Systems and methods described herein may provide a modelinginfrastructure that allows users to make models available to other usersand to search for and obtain models of interest.

The foregoing description of exemplary embodiments provides illustrationand description, but is not intended to be exhaustive or to limit theinvention to the precise form disclosed. Modifications and variationsare possible in light of the above teachings or may be acquired frompractice of the invention. For example, in some embodiments, modelinginfrastructure 220 may provide one or more application programminginterfaces (APIs) that allow different model creation providers tointegrate the use of modeling infrastructure 220 into their own modelcreation programs.

While the above description focused on modeling infrastructure 220providing a modeling sharing environment, modeling infrastructure 220may perform other functions. For example, modeling infrastructure 220may automatically search for models in a network, such as the Internet,and may automatically attempt to execute any models that are found. If amodel does not function properly (e.g., execution of the model fails,execution requires a large amount of resources, etc.), the model may beevaluated to determine if there is an issue with the model or with thesoftware that is executing the model. For example, if the model isvalid, there may be an issue with the software executing the model. Incase of a valid model, modeling infrastructure 220 may execute the modelusing a previous version of the potentially problematic software. If themodel functions properly using the previous version of software, themodel may be flagged for later analysis as to why the previous versionof software works, but the newer version of software does not work. Forexample, a comparison between the execution of the model using twodifferent versions of the software may reveal an increase in memoryusage, an increase in execution time, loading different sharedlibraries, etc. In this way, modeling infrastructure 220 may aid insoftware evaluation.

While series of acts have been described with regard to FIGS. 5, 8, 10,and 12, the order of the acts may be modified in other embodiments.Further, non-dependent acts may be performed in parallel.

It will be apparent that aspects, as described above, may be implementedin many different forms of software, firmware, and hardware in theembodiments illustrated in the figures. The actual software code orspecialized control hardware used to implement aspects described hereinis not limiting of the invention. Thus, the operation and behavior ofthe aspects were described without reference to the specific softwarecode—it being understood that one would be able to design software andcontrol hardware to implement the aspects based on the descriptionherein.

Further, certain portions of the invention may be implemented as “logic”that performs one or more functions. This logic may include hardware,such as an application specific integrated circuit or a fieldprogrammable gate array, software, or a combination of hardware andsoftware.

No element, act, or instruction used in the present application shouldbe construed as critical or essential to the invention unless explicitlydescribed as such. Also, as used herein, the article “a” is intended toinclude one or more items. Where only one item is intended, the term“one” or similar language is used. Further, the phrase “based on” isintended to mean “based, at least in part, on” unless explicitly statedotherwise.

1. A system comprising: means for receiving a search query from a modelcreation environment; means for performing a search of a repository thatincludes a plurality of models to identify a list of models based on thesearch query; means providing the identified list of models; means forproviding information for at least one model in the identified list ofmodels, the at least one model is a computational model, the informationincluding an author associated with the at least one model and a ratingof the at least one model; means for receiving a request for the atleast one model in the identified list of models; means for providingthe at least one model to the model creation environment based onreceiving the request; and means for inserting the at least one modelinto another computational model in the model creation environment. 2.An apparatus for searching for models, the apparatus comprising:processing logic coupled to an output device, wherein the processinglogic is programmed to: receive a search query from a model creationenvironment; perform a search of a repository that includes a pluralityof models to identify a list of models based on the search query;provide the identified list of models to the output device; provideinformation for at least one model in the identified list of models, theat least one model is a computational model, the information includingan author associated with the at least one model and a rating of the atleast one model; receive a request for the at least one model in theidentified list of models; provide the at least one model to the modelcreation environment based on receiving the request; and insert the atleast one model into another computational model in the model creationenvironment.
 3. A non-transitory computer-readable storage medium thatstores instructions executable by at least one processor for searchingfor models, the computer-readable storage device comprising:instructions for receiving a search query from a model creationenvironment; instructions for performing a search, of a repository thatincludes a plurality of models, to identify a list of models based onthe search query, the identified list of models including at least onecomputational model; instructions for providing the identified list ofmodels; instructions for providing information for the at least onecomputational model in the identified list of models, the informationincluding a rating of the at least one computational model, theinformation for the at least one computational model being providedwithin the model creation environment; instructions for receiving arequest for the at least one computational model in the identified listof models; and instructions for providing the at least one computationalmodel to the model creation environment based on receiving the request.4. The non-transitory computer-readable medium of claim 3 furthercomprising: instructions for associating one or more tags with theplurality of models; and instructions for comparing the search query tothe one or more tags.
 5. The non-transitory computer-readable medium ofclaim 3 further comprising: instructions for inserting the at least onecomputational model into another computational model in the modelcreation environment.
 6. The non-transitory computer-readable medium ofclaim 3 wherein the search query includes at least one search criterion,and the at least one search criterion is selected from the groupconsisting of: a level of complexity for a desired model, a number ofwarnings that an editing style is violated with respect to the desiredmodel, a number or range of numbers of input ports contained in thedesired model, a number or range of numbers of output ports contained inthe desired model, a date or date range when the desired model was madeavailable, an author of the desired model, a location or group oflocations where a search using the search query is to be performed,rating information for the desired model, whether the desired model usescontinuous time integration or discrete time integration, whether thedesired model is self-contained, a number of subsystems within thedesired model, information identifying one or more modeling standards towhich the desired model adheres, information identifying a product orproducts on which the desired model relies, annotations added to thedesired model, and a version of software that was used to create thedesired model.
 7. A non-transitory computer-readable storage medium thatstores instructions executable by at least one processor for searchingfor models, the computer-readable storage device comprising:instructions for receiving a search query from a model creationenvironment; instructions for performing a search, of a repository thatincludes a plurality of models, to identify a list of models based onthe search query, the identified list of models including at least onecomputational model; instructions for providing the identified list ofmodels; instructions for providing information for the at least onecomputational model in the identified list of models, the informationselected from the group consisting of a rating of the at least onecomputational model, a size of the at least one computational model, anda version of the at least one computational model, the information forthe at least one computational model being provided within the modelcreation environment; instructions for receiving a request for the atleast one computational model in the identified list of models; andinstructions for providing the at least one computational model to themodel creation environment based on receiving the request.
 8. Thenon-transitory computer-readable medium of claim 7 further comprising:instructions for associating one or more tags with the plurality ofmodels; and instructions for comparing the search query to the one ormore tags.
 9. The non-transitory computer-readable medium of claim 7further comprising: instructions for inserting the at least onecomputational model into another computational model in the modelcreation environment.
 10. The non-transitory computer-readable medium ofclaim 7 wherein the search query includes at least one search criterion,and the at least one search criterion is selected from the groupconsisting of: a level of complexity for a desired model, a number ofwarnings that an editing style is violated with respect to the desiredmodel, a number or range of numbers of input ports contained in thedesired model, a number or range of numbers of output ports contained inthe desired model, a date or date range when the desired model was madeavailable, an author of the desired model, a location or group oflocations where a search using the search query is to be performed,rating information for the desired model, whether the desired model usescontinuous time integration or discrete time integration, whether thedesired model is self-contained, a number of subsystems within thedesired model, information identifying one or more modeling standards towhich the desired model adheres, information identifying a product orproducts on which the desired model relies, annotations added to thedesired model, and a version of software that was used to create thedesired model.
 11. A non-transitory computer-readable storage devicethat stores instructions executable by at least one processor forsearching for models, the computer-readable storage device comprising:instructions for receiving a search query from a model creationenvironment; instructions for performing a search, of a repository thatincludes a plurality of models, to identify a list of models based onthe search query, the identified list of models including at least onecomputational model; instructions for providing the identified list ofmodels; instructions for providing first information for the at leastone computational model in the identified list of models, the firstinformation selected from the group consisting of: an author of the atleast one computational model, a rating of the at least onecomputational model, a size of the at least one computational model, anda version of the at least one computational model; instructions forreceiving a selection of the at least one computational model in theidentified list of models; and instructions for providing secondinformation for the at least one computational model in the identifiedlist of models, the second information selected from the groupconsisting of: one or more reviews of the at least one computationalmodel, an indication of a quality of the at least one computationalmodel, and one or more tags associated with the at least onecomputational model.
 12. The non-transitory computer-readable storagedevice of claim 11 wherein the indication of a quality is selected fromthe group consisting of: a cyclomatic complexity of the at least onecomputational model, a number of semantic errors in the at least onecomputational model, a number of syntax errors in the at least onecomputational model, a number of potential problems with the at leastone computational model, an amount of help files associated with the atleast one computational model, a number of issues that exist withrespect to file naming or an amount of duplicate functionality includedin the at least one computational model, and an indication as to how theat least one computational model compares to one or more other models.13. The non-transitory computer-readable storage device of claim 11wherein the selection is received from a user input device.
 14. Thenon-transitory computer-readable storage device of claim 11 wherein theuser input device is selected from the group consisting of a keyboard, amouse, a pen, and a voice recognition unit.
 15. A non-transitorycomputer-readable storage device that stores instructions executable byat least one processor for searching for models, the computer-readablestorage device comprising: instructions for receiving a search queryfrom a model creation environment; instructions for performing a search,of a distributed repository storing a plurality of models, to identify alist of models based on the search query, the identified list of modelsincluding at least one computational model, the distributed repositorydistributed across a plurality of server entities; instructions forproviding the identified list of models; instructions for providinginformation for the at least one computational model in the identifiedlist of models, the information selected from the group consisting of anauthor of the at least one computational model, a rating of the at leastone computational model, a size of the at least one computational model,and a version of the at least one computational model, the informationfor the at least one computational model being provided within the modelcreation environment; instructions for receiving a request for the atleast one computational model in the identified list of models; andinstructions for providing the at least one computational model to themodel creation environment based on receiving the request.